Recently, some datasets and new technologies have been proposed for real SISR. In [1]}, the RealSR dataset is proposed, where paired LR-HR images on the same scene are captured by adjusting the focal length of a digital camera. Meanwhile, a Laplacian Pyramid based Kernel Prediction Network (LP-KPN) is trained with this dataset to learn per-pixel kernels to recover SR images. After that, a series of real image pairs-based methods [2]}, [3]}, [4]} are proposed. However, this dataset are post-processed and difficult to collect in large quantities, which still limits the model performance. Otherwise, some new technologies have been proposed, such as unsupervised learning [5]}, [6]}, self-supervised learning [7]}, [8]}, zero-shot learning [7]}, [10]}, meta-learning [11]}, [12]}, blind SISR, and scale arbitrary SISR [13]}, [14]}. In this part, we introduce the latter three methods due to their impressive foresight and versatility.
